The most used problem to compare these metaheuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optim...
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(纸本)9798350381603
The most used problem to compare these metaheuristics is the classical Travelling Salesman Problem (TSP) on a network with sizes from tens to millions of nodes. Nature-based metaheuristics are the main source of optimization techniques to solve this problem. Although there is a zoo of possibilities, antcolonyoptimization (ACO) is still one of the most efficient, parallel, and simple techniques to implement. The pheromone evaporation rate, alpha,beta, and ant number M are the four parameters fitted for finding the best solutions. Candidate list and the use of a source solution are efficient strategies to optimize large problems, but there are other strategies to improve quality solutions such as local search strategies. Among the local search techniques, k-opt search has proved to be very efficient to deal with path-crossing. The initial route is split into k sub-routes connected in (k- 1)!2(k-1) ways. Thus, 3-opt is an efficient strategy balancing complexity and precision. Moreover, the best alpha and beta are the result of the used strategy. Finally, the solution is improved by accelerating through parallel versions with multiGPUs. In this article, four ACO algorithms were developed using two variations (Rank Based 3-opt and Strong Elitist 3-opt), each one with two strategies (candidate list and restricted). To test the algorithms, TSPLIB95 was used with test problems between N = 51 and 4, 461 nodes in a server with 4 RTX A4000 GPUs. After improving the algorithms and parallelization, the parameters are tuned to get the highest performance improving the local minimum search. Statistical analysis of repetitions shows good stability of the chosen metaheuristics.
Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in un...
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Because of the complicated underwater environment, the efficiency of data transmission from underwater sensor nodes to a sink node (SN) is faced with great challenges. Aiming at the problem of energy consumption in underwater wireless sensor networks (UWSNs), this paper proposes an energy-efficient clustering routing algorithm based on an improved antcolonyoptimization (ACO) algorithm. In clustering routing algorithms, the network is divided into many clusters, and each cluster consists of one cluster head node (CHN) and several cluster member nodes (CMNs). This paper optimizes the CHN selection based on the residual energy of nodes and the distance factor. The selected CHN gathers data sent by the CMNs and transmits them to the sink node by multiple hops. Optimal multi-hop paths from the CHNs to the SN are found by an improved ACO algorithm. This paper presents the ACO algorithm through the improvement of the heuristic information, the evaporation parameter for the pheromone update mechanism, and the ant searching scope. Simulation results indicate the high effectiveness and efficiency of the proposed algorithm in reducing the energy consumption, prolonging the network lifetime, and decreasing the packet loss ratio.
This paper investigates the possibility of solving two problems of digital device diagnostics by means of antcolonyoptimization. It is shown how these problems can be reduced to the classical traveling salesman and ...
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This paper investigates the possibility of solving two problems of digital device diagnostics by means of antcolonyoptimization. It is shown how these problems can be reduced to the classical traveling salesman and set covering problems which are efficiently solved by ant colony optimization algorithms.
antcolonyoptimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper propo...
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antcolonyoptimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. A...
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Previous studies have emphasized the potential of threshold image segmentation for early breast cancer detection. However, traditional methods encounter challenges regarding low segmentation efficiency and accuracy. Addressing this, the antcolonyoptimization algorithm for continuous optimization (ACOR) shows promise. Yet, existing ACOR variants still grapple with poor initial population quality, affecting convergence speed and avoiding local optimization. These issues impact segmentation efficiency and accuracy. To tackle them, this study introduces RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local optima entrapment and enhancing model stability. SCM expediting convergence, segmentation efficiency, and precision. RESACO's performance is assessed through extensive experiments using IEEE CEC 2014 and IEEE CEC 2022 benchmark functions, including ablation experiments and comparisons with basic and improved algorithms and ACOR variants. Subsequently, the threshold image segmentation model based on RESACO is compared with other models using metaheuristic algorithms for segmenting realistic breast cancer medical images. Results demonstrate the proposed model's faster convergence and higher segmentation accuracy, preserving more lesion tissue details. RESACO, an enhanced ACOR version integrating three novel optimization strategies: resampling initialization (RIS), elite exploration (EES), and strengthened convergence mechanism (SCM). RIS enhances initial population quality by resampling regions with individuals demonstrating superior fitness and segmentation efficiency. EES promotes exploration across the search space, preventing local
optimization for active debris removal using multiple spacecraft is investigated. The main challenge is to determine the rendezvous sequences of the targets considering the J(2) perturbation, which is a large-scale dy...
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optimization for active debris removal using multiple spacecraft is investigated. The main challenge is to determine the rendezvous sequences of the targets considering the J(2) perturbation, which is a large-scale dynamic combinatorial optimization problem. A framework to solve this problem is presented. First, a semi-analytical method of fast estimation for characteristic velocity is proposed to help construct the sequences. The K-medoid method is applied to distribute all the targets to multiple spacecraft so that each spacecraft can remove as many targets as possible with limited propellant. Given a distribution of targets, the sequences for each spacecraft are searched separately;thus the original combinatorial optimization problem is split into multiple small-scale ones that can be efficiently solved by tree search algorithms. An evolutionary algorithm nested with a tree search algorithm is proposed to improve the distribution of the targets, so that the spacecraft can remove all the targets with a low cost. Then optimal sequences are further searched using an antcolonyoptimization algorithm, and the rendezvous epochs are refined by a nonlinear programming algorithm. The efficiency of these methods is demonstrated by two scenarios of active debris removal.
In graph theory, the k-minimum spanning tree problem is considered to be one of the well-known NP hard problems to solve. This paper address this problem by proposing several hybrid approximate approaches based on the...
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In graph theory, the k-minimum spanning tree problem is considered to be one of the well-known NP hard problems to solve. This paper address this problem by proposing several hybrid approximate approaches based on the combination of simulated annealing, tabu search and ant colony optimization algorithms. The performances of the proposed methods are compared to other approaches from the literature using the same well-known library of benchmark instances.
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